# -*- coding: utf-8 -*-
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np

"""
MNIST数据集，每一张图片包含28X28个像素点
"""


def cnn_model_fn(features, labels, mode):
    """
    构建模型函数
    
    """
    # 输入层
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
    # 卷积层1
    conv1 = tf.layers.conv2d(inputs=input_layer, filters=32, kernel_size=[5, 5], padding='same', activation=tf.nn.relu)
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
    # 卷积层2
    conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5], padding='same', activation=tf.nn.relu)
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # Dense Layer
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
    dense1 = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
    dropout = tf.layers.dropout(inputs=dense1, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

    # Logits Layer
    logits = tf.layers.dense(inputs=dropout, units=10)
    predictions = {
        "classes": tf.argmax(input=logits, axis=1),
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }

    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    # 损失函数
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

    eval_metric_ops = {"accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions["classes"])}
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


def main():
    mnist = tf.contrib.learn.datasets.load_dataset("mnist")
    train_data = mnist.train.images  # Returns np.array
    train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
    eval_data = mnist.test.images  # Returns np.array
    eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

    mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, model_dir="./models/tf_mnist_convnet_model")

    tensors_to_log = {"probabilities": "softmax_tensor"}
    logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)

    # Train the model
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"x": train_data},
        y=train_labels,
        batch_size=100,
        num_epochs=64,
        shuffle=True
    )

    mnist_classifier.train(input_fn=train_input_fn, hooks=[logging_hook], steps=2000)

    # Evaluate the model
    eval_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"x": eval_data},
        y=eval_labels,
        num_epochs=1,
        shuffle=False)
    eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
    print(eval_results)



if __name__ == '__main__':
    main()
